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date: 23 November 2017

Socio-Hydrology of Floods

Summary and Keywords

Fatalities and economic losses caused by floods are dramatically increasing in many regions of the world, and there is serious concern about future flood risk given the potentially negative effects of climatic and socio-economic changes. Over the past decades, numerous socio-economic studies have explored human responses to floods—demographic, policy and institutional changes following the occurrence of extreme events. Meanwhile, many hydrological studies have investigated human influences on floods, such as changes in frequency, magnitude, and spatial distribution of floods caused by urbanization or by implementation of risk reduction measures. Research in socio-hydrology is providing initial insights into the complex dynamics of risk resulting from the interplay (both responses and influences) between floods and people. Empirical research in this field has recently shown that traditional methods for flood risk assessment cannot capture the complex dynamics of risk emerging from mutual interactions and continuous feedback mechanisms between hydrological and social processes. It has also been shown that, while risk reduction strategies built on these traditional methods often work in the short term, they might lead to unintended consequences in the longer term. Besides empirical studies, a number of socio-hydrological models have been recently proposed to conceptualize human/flood interactions, to explain the dynamics emerging from this interplay, and to explore possible future trajectories of flood risk. Understanding the interplay between floods and societies can improve our ability to interpret flood risk changes over time and contribute to developing better policies and measures that will reduce the negative impacts of floods while maintaining the benefits of hydrological variability.

Keywords: human-flood interactions, socio-hydrological modeling, uncertainty, flood risk, disaster risk reduction

Floods and Societies

Premise

This article provides an overview of the socio-hydrology of floods—understanding the dynamics of flood risk resulting from the interactions between hydrological and social processes—and puts this topic in the larger context of natural hazards and risk assessment.

Over the past few years, there has been increasing interest in socio-hydrology (e.g., Blair & Buytaert, 2016; Di Baldassarre, Yan, Ferdous, & Brandiamrte, 2013a; Di Baldassarre, Viglione, Carr, Kuil, Salinas, & Blöschl, 2013b; Elshafey, Sivapalan, Tonts, & Hipsey, 2014; Gober & Wheater, 2015; Loucks, 2015; Montanari et al., 2013; Pande & Savenije, 2016; Sivapalan & Bloeschl, 2015; Sivapalan, Savenije, & Blöschl, 2012; Srinivasan, Lambin, Gorelick, Thompson, & Rozelle, 2012; Troy, Pavao-Zuckerman, & Evans, 2015; van Emmerik et al., 2014; Viglione et al., 2014), which has built upon integrated water resources management (IWRM) while drawing from a variety of inter-disciplinary frameworks, exploring the mutual shaping of society and nature, such as social-ecological systems (SES) and complex system theories (e.g., Adger, Hughes, Folke, Carpenter, & Rockström, 2005; Liu et al., 2007; Ostrom, 2009; Werner & McNamara, 2007). Socio-hydrology of floods has received much attention given its societal relevance—the need to develop better policies for disaster risk reduction and for sustainable development in a rapidly changing world. Also, the UN Sendai Framework (2015) indicates “understanding disaster risk” as Priority number 1.

To introduce the socio-hydrology of floods, this first section (“Floods and Societies”) summarizes studies of the human impacts on (and responses to) floods. “Socio-Hydrology of Floods” illustrates the current state of socio-hydrological science by providing examples of observations and modeling of human-flood interactions. Last, “Risk, Uncertainty, and Open Questions” discusses the implications (and open research questions) for flood risk assessment in a changing climate.

Human Influences and Responses

Humans have significantly altered the frequency, magnitude, and spatial distribution of flood events. This alteration has been deliberate or accidental. Dams and reservoirs are examples of water management measures that deliberately change hydrological variability and significantly affect the severity of flood events. Also, flood protection measures, such as levees, alter the frequency, magnitude, and spatial distribution of flood events (Blöschl, Nester, Komma, Parajka, & Perdigão, 2013; Di Baldassarre et al., 2009; Heine & Pinter, 2012). Floods are also influenced by other human activities, such as land-use change, including deforestation, urbanization, drainage of wetlands and agricultural practices (Savenije et al., 2014).

While societies shape the characteristics of flood events, hydrological extremes (in turn) shape societies, in terms of institutions, governance, and demography (e.g., Myers, Slack, & Singelmann, 2008). Following the impact of flood events, humans respond and adapt to hydrological extremes through a combination of spontaneous processes and deliberate strategies that can lead to changes in social vulnerability (Adger, Quinn, Lorenzoni, Murphy, & Sweeney, 2013). Adaptive responses can take place at the individual, community, or institutional level. Besides informal adaptive processes, such as temporary and permanent migration, flood events can trigger changes of risk management policies, which also impact social vulnerability (Pahl-Wostl, Becker, Knieper, & Sendzimir, 2013). Early warning systems, risk awareness programs, and changes of land-use planning are examples of adaptive responses that often occur at the local or central government level following major flood events (Di Baldassarre et al., 2015). Moreover, structural risk reduction measures, such as dams and levees, are also planned, implemented or revised after the occurrence of flooding, and they in turn (again) change the frequency, magnitude and spatial distribution of future flood events (Di Baldassarre et al., 2013a).

Socio-Hydrology of Floods

The previous sub-section has shown that human societies influence the frequency and magnitude of floods, while at the same time the frequency and magnitude of floods simultaneously shape human societies. Socio-hydrology aims to uncover these feedback mechanisms (Figure 1) and explore the emerging dynamics.

Socio-Hydrology of FloodsClick to view larger

Figure 1. Feedback Mechanisms Between Floods and Societies. Internal Interactions Within Floodplains as Human-Water Systems, and External Drivers of Change at Regional or Global Scale (e.g., Climate Change and Variability and Socio-Economic Trends).

The socio-hydrological cycle of Figure 1 can be used to explain, for example, the so-called “levee effect” (White, 1945). Many societies in the Global North build and raise levees to protect floodplain areas and therefore reduce the frequency of flooding. The reduced frequency of flooding can trigger intensive urbanization (or industrialization) of flood-prone areas, which are then vulnerable to rare-but-catastrophic disasters. This effect has been observed in various places around the world and can explain why, paradoxically, flood control structures might even increase risk, as protection from frequent flooding shapes perceptions of risk and tends to promote additional human settlements in floodplain areas (Burton & Cutter, 2008; Di Baldassarre, Kooy, Kemerink, & Brandimarte, 2013a; Di Baldassarre et al., 2013b; Di Baldassarre et al., 2015; Ludy & Kondolf, 2012; Montz & Tobin, 2008).

There is ongoing discussion on the sustainability of continuous levee heightening given the associated self-reinforcing feedback loop: higher levels of flood protection can trigger intense urbanization of flood-prone areas, which in turn leads to the need for higher levels of flood protection. Various regions in the Global North, such as California, have started to consider giving back some room to the river via floodplain reconnection (Opperman, Galloway, Fargione, Mount, Richter, & Secchi, 2009). Meanwhile, many countries the Global South, such as Bangladesh, seems to be following a similar trajectory, as donors and development agencies often support the construction of flood protection structures, such as levees or dikes.

Understanding the dynamics of flood risk emerging from the interplay of hydrology and society, such as the levee effect, is one of the main goals of the socio-hydrology of floods. A secondary goal, perhaps even more ambitious, is to capture long-term trajectories of human-flood systems. Figure 2 shows three conceptual examples to highlight that, when it comes to the wealth or wellbeing of a community, recovery trajectories can matter more than the direct damage caused by flood events. The socio-hydrology of floods aims to identify the main driving factors leading to different outcomes, such as collapse (Scheffer, Carpenter, Foley, Folke, & Walker, 2001), and bounce back or forward (Figure 2). In this context, a fundamental research question is: are there hidden elements of resilience that make a system bouncing back or forward after the occurrence of extreme flooding?

Socio-Hydrology of FloodsClick to view larger

Figure 2. Flood Disasters and Recovery Trajectories: Theoretical Growth of Wealth Over Time (Dotted Black Line) and Possible Trajectories Following a Flood Disaster (Solid Grey Line).

Socio-Hydrology of Floods

Observations

This sub-section provides empirical evidence of two types of risk dynamics resulting from the feedbacks between human and flood systems: (a) learning or adaptation effects, and (b) forgetting or levee effects.

Learning or adaptation effects relate to the observation that the frequent occurrence of flooding is often associated with decreasing vulnerability (Figure 3, top panel). For instance, the negative impact of a flood event tends to be significantly lower when such an event occurs shortly after a similar one. Various examples of this type of dynamic have been described by the literature (IPCC, 2012; Mechler & Bouwer, 2015; Penning-Rowsell, 1996) and were recently summarized by Di Baldassarre et al. (2015). For instance, the economic losses of the 1995 flooding at the Meuse River were remarkably lower than those in 1993, even though the severity of the two events was similar (Wind et al., 1999). Mechler and Bouwer (2015) showed a similar trend by analyzing flood fatalities in Bangladesh (data are used in Figure 3, top panel). This learning effect can be attributed to enhanced coping or adaptation capacities gained by individuals and communities during their flood experience. In particular, there is often a combination of informal processes (mainly at the individual level) and policy change of flood risk management as a response to major flood events (Johnson, Tunstall, & Penning-Rowsell, 2005; Pahl-Wostl et al., 2013; Penning-Rowsel, Johnson, & Tunstall, 2006). Formal adaptation measures occurring at the local or central government level following a flood event can include the introduction of early warning systems, the development of community engagement programs to raise awareness to flood risk, and changes in land use planning.

Socio-Hydrology of FloodsClick to view larger

Figure 3. Adaptation and Levee Effects. The top panel shows the decreasing ratio between flood fatalities and flooded area in Bangladesh, which is affected by numerous and frequent events (data from Mechler & Bouwer, 2015). The bottom panel shows the increasing population in flood-prone areas in Rome (Italy), as levees were built after a flood disaster in 1870; no major flooding has occurrence since then (data from Di Baldassarre et al., 2016).

Forgetting or levee effects relate to the observation that the rare occurrence of flooding (possibly caused by protection measures, such as levees) is often associated with increasing vulnerability (Figure 3, bottom panel). This type of dynamics has been discussed in the literature since White (1945), and there is empirical evidence that flood control structures tend to trigger an increase of the potential losses. The city of Rome is an example provided here (Figure 3, bottom panel). The process of building levees started after a major flooding in 1870, and this has led to increasing population in flood-prone areas. Additional examples have been described (implicitly or explicitly) by the literature (Bohensky & Leitch, 2014; de Moel, Aerts, & Koomen, 2011; Di Baldassarre et al., 2013a; IPCC, 2012; Kates, Colten, Laska, & Leatherman, 2006; Ludy & Kondolf, 2012; Werner & McNamara, 2007) and were recently summarized by Di Baldassarre et al. (2015).

It should be mentioned that the frequency of flooding can be reduced not only by the introduction or strengthening of flood protection measures, but also by climate variability and change. Thus, as a paradox, the emergence of forgetting effects suggests that areas in which flood frequency is projected to decrease (e.g., many rivers basins in Finland) will not necessarily experience less flood losses. In fact, there might be hidden elements of flood resilience that get lost with time if flood events become rarer.

Modeling

Over the past few years, the scientific literature has proposed a number of conceptual models of human-flood interactions (e.g., Di Baldassarre et al., 2013a; Di Baldassarre et al., 2015; Grames, Prskawetz, Grass, & Blöschl, 2015; O’Connell & O’Donnell, 2014; Viglione et al., 2014). In very general and simplified terms, human-flood interactions can be expressed by using differential equations:

dHdt=f2(S,...)

dSdt=f2(H,...)

Where H and S are two variables related to, respectively, some characteristics or proxies of hydrological and social processes, respectively (see also the feedback loop of Figure 1). The first equation expresses changes in time of hydrological extremes as a function (f1) of a social variable S and other drivers of hydrological change. The second equation expresses social changes in time as a function (f2) of a hydrological variable H and other drivers of social change. Additional equations might be needed, depending on the complexity of the dynamic model, and fast-slow dynamics can be accounted for (see discussion in Sivapalan & Bloeschl, 2015). Socio-hydrological models aim to treat hydrological and social processes with the same level of complexity. Moreover, also in view of their use (explaining dynamics more than making quantitative predictions), these conceptual models are typically built following a parsimonious approach—“as simple as possible, but not simpler.”

Di Baldassarre et al. (2013b; Di Baldassarre et al., 2015) and Viglione et al. (2014) conceptualized the interplay between social vulnerability and flood events by using the concept of social memory (e.g., Folke, Hahn, Olsson, & Norberg, 2005), which is assumed to be built after the experience of flood events and then decay over time. They also consider two prototypes of floodplain systems: (a) green systems, in which societies respond by reducing vulnerability and exposure to flooding via non-structural measures (living with floods); and (b) technical systems, in which societies rely also on structural measures, including levees, to reduce flood hazard (fighting floods). In these models, the changes in the spatial distribution of human population account are driven by trade offs between the benefits of settling in floodplain areas (e.g., agriculture, trade) and the potential costs in case of flooding (e.g., flood fatalities, economic losses).

To show an example application of socio-hydrological modeling of floods, Figure 4 shows the results presented by Di Baldassarre et al. (2015) in modeling the impact of increasing flood levels (Figure 4A), which might be caused by climate change or sea level rise. The model simulates the behavior of green and technical systems.

By analyzing Figure 4, the most striking result is that the model is able to capture the dynamics resulting from the mutual shaping of floods and societies, that is the aforementioned adaptation and levee effects (Panels D and H).

Socio-Hydrology of FloodsClick to view larger

Figure 4. Coupled Dynamics of Floods and Societies in Response to Increasing Flood Levels. Effect on green society: (A) Actual high water levels, (B) levee heights, (C) memory, (D) flood losses. Effect on technological society: (E) Actual high water levels (note the enhancement due to the presence of levees, which is highlighted in red), (F) levee heights, (G) memory, (H) flood losses

(Source: Di Baldassarre et al., 2015).

Learning or adaptation effects dominate the dynamics of the green system from 1950 to 1980, when similar flood levels (Figure 4A) lead to decreasing losses (Figure 4D). This dynamic occurs because social memory is built with each flood experience (Figure 4C), and this reduces urban development in flood-prone areas (i.e., population density growth rate), which in turn reduces flood losses.

For the technical society, forgetting or levee effects emerge from 1955 to 2045, as moderate flood levels (Figure 4E) do not cause any damage (Figure 4H) because flooding is prevented by the presence of high levees (Figure 4F). This absence of flooding, however, leads to a reduction of flood memory (Figure 4G) and population growth in flood-prone areas. When an exceptionally high flood occurs in 2048, levees are overtopped and losses are huge (around 70% in relative terms; Figure 4H). These losses can be catastrophic. In addition, Figure 4E shows the enhancement of flood levels due to the presence of levees, feedback on the hydrology of floods (Heine & Pinter, 2012).

Green systems experience flooding more frequently. However, despite the dramatic trend in flood levels, losses remain limited between 5% and 25% (Figure 4D). Thus, the main result of the exercise summarized in Figure 4 is that, despite increasing flood levels, green systems are affected by relatively small flood losses, while technological societies are prone to rare, but catastrophic losses. This difference is explained by the role of social memory (e.g., Folke, Hahn, Olsson, & Norberg, 2005), which is often refreshed in green systems via frequent experience of floods. In contrast, in technical systems, flood memory decays as many high water levels do not produce any flooding. Long flood-poor periods, which are artificially induced here by building and raising levees, can have a major effect on flood risk dynamics with potentially catastrophic consequences.

Risk, Uncertainty and Open Questions

Implications for Flood Risk Assessment

While learning and forgetting effects (section “Socio-Hydrology of Floods”) have been observed in various floodplains and deltas around the world, many traditional methods for risk assessment cannot capture these dynamics. Changes in flood risk are typically assessed by comparing scenarios of climatic and socio-economic changes (Apel, Aronica, Kreibich, & Thieken, 2009; Winsemius, Van Beek, Jongman, Ward, & Bouwman, 2013; Winsemius et al., 2015). For each scenario, flood risk is estimated as a combination of flood hazard and societal exposure and vulnerability (or resilience) to floods. Policies, such as the implementation of flood protection measures, are often treated as an external forcing to the flood system; while the losses caused by the physical system are treated as an external forcing to the human system (Figure 5; top panel). Thus, traditional approaches cannot explicitly account for the continuous, dynamic interplay between water and human systems. As a result, they cannot capture the dynamics emerging from the mutual shaping of floods and societies, such as learning and forgetting effects. For instance, most methods would consistently suggest that flood-rich periods would lead to more flood losses. However, the learning effect shows that this is not necessarily the case. Similarly, most methods would consistently suggest that the implementation of flood protection measures would lead to less flood losses (Jongman et al., 2014), but the forgetting effect shows that this is not always the case.

Socio-Hydrology of FloodsClick to view larger

Figure 5. Changes in Flood Risk. Most traditional approaches (top panel) are based on scenarios. For each time slice, risk is estimated as a combination of flood hazard and societal exposure and vulnerability. The dynamics emerging from the feedbacks between hydrological and social processes—such as adaptation and levee effects—cannot be captured. Socio-hydrological approaches (bottom panel) capture the coupled dynamics of floods and societies and the long-term behavior emerging from the mutual interactions and feedbacks between social and physical systems.

Socio-hydrological approaches (Figure 5, bottom panel) can be used to complement traditional methods, as they enable accounting for coupled dynamics of floods and societies and for capturing the long-term behavior emerging from the mutual interactions and feedbacks between social and physical systems. Yet, there remain the challenges associated with the unpredictability of human behavior (Di Baldassarre, Brandimarte, & Beven, 2016), as well as difficulty in the quantification of various variables, such as social memory. Moreover, social perception of flood risk can vary strongly across human societies, and depends not only on endogenous factors, such as the accumulation of memory that follows the occurrence of flooding, but also on exogenous factors, such as political conditions and cultural values (e.g., Eiser et al., 2012; Wachinger et al., 2012). Thus, while socio-hydrological approaches have the advantage of being potentially more realistic in explaining the dynamics of risk, they tend to provide insights that are more qualitative (Driscoll, Appiah-Yeboah, Salib, & Rupert, 2007) than the ones obtained with traditional methods of flood risk assessment. Thus, traditional and novel methods depicted in Figure 5 can then be seen as complimentary.

Uncertainty and Surprises

The study of human/flood interactions is affected by numerous sources of aleatory and epistemic uncertainty (Di Baldassarre et al., 2016), which are difficult to identify. To illustrate this challenge, Figure 6 shows the time series of annual maximum water levels recorded at Ponte delle Alpi and includes the surprisingly high (and essentially unpredictable) flood level of October 9, 1963, which destroyed the hydrological station. An unrepeatable cascade of contingencies and chain of events generated this incredibly flood level. In particular, an artificial lake was created upstream from the hydrometric station by the newly built Vajont Dam. During one of the initial tests of the reservoir, an immense and fast landslide fell into the lake and displaced 50 million m3 of water. Giant waves from the lake overtopped the dam, destroyed the town of Longarone located downstream and killed about 2,000 people (Bianchizza & Frigerio, 2013; Delle Rose, 2012; Di Baldassarre, Yan, Ferdous, & Brandimarte, 2014; Ward & Day, 2011). This huge volume of water generated a giant flood, which then propagated along the Piave River and led to the surprisingly high water level of October 9, 1963, which washed away the hydrological station of Ponte delle Alpi (Viparelli & Merla, 1968). While this incredibly high flood level would not be used in traditional hydrological studies that focus on flood processes, socio-hydrological research should also consider the possibility of surprises, such as the one depicted in Figure 6 can be generated.

Socio-Hydrology of FloodsClick to view larger

Figure 6. Flood Levels at Ponte Delle Alpi, Italy. Time series of maximum annual water levels recorded at the hydrological station and the (essentially unpredictable) surprisingly high water level caused by an upstream man-made tsunami—the Vajont dam disaster

(Source: Di Baldassarre et al., 2016).

Being aware of potential surprises is key when socio-hydrological models support the decision-making process in flood risk management. Unexpected events or black swans (Taleb, 2007) remind about the importance of reducing the negative impacts of extreme events (Makridakis & Taleb, 2009a, 2009b). Reducing vulnerability (and enhancing resilience) of human societies can be more robust than heavily relying on predictions of the close-to-zero (basically unknown) probability of disasters caused by unrepeatable cascades of contingencies or unique combinations of contexts. The development of evacuation and contingency plans, for example, does not strictly require an accurate and precise estimation of flood scenarios or probabilities, but it can significantly improve the ability of the human-water system to recover after a disaster (Di Baldassarre et al., 2016).

Possible surprises or black swans (Taleb, 2007) highlight the need to complement top-down approaches, such as the ones depicted in Figure 5, with bottom-up approaches, based on social vulnerabilities and possibilities of failures (Blöschl et al., 2013; Di Baldassarre et al., 2016). Bottom-up approaches do not start from risk scenarios, but, rather, from the social and economic vulnerability of communities and individuals; they then explore the possibilities of failures by explicitly considering the expertise of local stakeholders and risk managers (Blöschl et al., 2013; Lane et al., 2011; Merz et al., 2015).

Open Questions

The socio-hydrological approach for the study of changes of flood risk is not only scientifically appealing, but also socially relevant. For instance, the UN Sendai Framework for Disaster Risk Reduction (2015) indicates “understanding disaster risk” as “Priority 1.” By unravelling the mutual shaping of floods and societies, socio-hydrological approaches can complement traditional methods (Figure 5) and provide valuable insights about the way in which the different components of risk (flood hazard, vulnerability, and exposure) continuously coevolve and change over time. In a rapidly changing world, this will support the development of policies and strategies that will maintain the ecological benefits of hydrological variability, while reducing the negative impacts of flood events, such as fatalities and economic losses. There remain, however, a number of open research questions.

First, while forgetting and learning dynamics have been observed in a number of places around the world, it is still unknown whether they are site-specific effects or generic dynamics emerging under a given set of social and hydrological circumstances. Also, the way in which the coevolution of floods and societies unfolds is only described in narratives for specific case studies. Thus, there is a need to explore multiple river basins, floodplains, or cities as coupled human-water systems to better understand how human societies shape the frequency, magnitude, and spatial distribution of flood events (accidentally or deliberately via policies and measures of sustainable water management, urban planning, and disaster risk reduction), while at the same time the impacts and perceptions of hydrological extremes shape society (in terms of demography, policy, institution, and governance). The current proliferation of worldwide datasets and global remote sensing data offers an unprecedented opportunity to perform this type of study (Di Baldassarre et al., 2013a).

Second, there is a need to link flood socio-hydrology with research on anthropogenic drought (AghaKouchak, Feldman, Hoerling, Huxman, & Lund, 2015; Van Loon, Gleeson, Clark, van Dijk, Stahl, Hannaford et al., 2016). While vulnerability-based methods (Turner et al., 2003) often account for both hydrological extremes, hazard-based methods for quantitative risk assessment (Figure 5) focus on either drought or flood risk. This does not allow exploring key dynamics of risk. For instance, water management rules (Di Baldassarre et al., 2016; Mateo et al., 2014) that reduce drought risk are different from the ones that reduce flood risk, and these rules often change over time depending on various factors, including whether the most recently experienced disaster was caused by a drought or a flood event. As a result, the negative impact of flood events occurring immediately after a long period of drought conditions might be exacerbated. For instance, reservoirs reduce hydrological variability and potentially mitigate both floods and droughts. Yet, to mitigate flood events, reservoirs should be kept as empty as possible; whereas, to mitigate drought events, reservoirs should be kept as full as possible. Thus, different reservoir operational rules correspond to a focus on flood or drought events. The catastrophic 2011 flooding of Brisbane occurred immediately after a multi-year drought (so-called “Millennium Drought”; Van Dijk et al., 2013), which had triggered changes in reservoir management (van den Honert & John McAneney, 2011); that is, a flood mitigation reservoir was used as a buffer to cope with low flow conditions. This change in reservoir operational rules might have exacerbated the impact of the 2011 flood event. Research on climate change (IPCC, 2014) suggests that many regions around the world might experience, in the near future, more prolonged drought conditions followed by extreme flood events. Thus, it is key to understand if (and how) human responses to drought events might exacerbate the impact of future floods, and vice versa.

Last, focusing on flood risk can limit the interpretation of the role of global drivers of change, such as climate, on hydrological risk. For example, a number of recent studies (e.g., Di Baldassarre, Montanari, Lins, Koutsoyiannis, Brandimarte, & Blöschl, 2010; Winsemius et al., 2015) have shown that growing populations in floodplain areas have been the main driver of increasing flood risk in Africa, while climate change has (so far) played a smaller role. Yet, by focusing on flood risk only, these studies did not consider the plausible hypothesis that, in some instances, climate change may have led to longer and more severe drought conditions, which have in turn enhanced the need for communities to get closer to rivers, leading to higher exposure to flooding. Thus, it is still unknown, how different sequences of drought and flood events make a difference in the dynamics of hydrological risk. This puzzle requires further research on the mutual shaping of human societies and hydrological extremes.

References

Adams, V. (2012). The other road to serfdom: Recovery by the market and the affect economy in New Orleans. Public Culture, 24, 185–216.Find this resource:

Adger, W. N., Hughes, T. P., Folke, C., Carpenter, S. R., & Rockström, J. (2005). Social-ecological resilience to coastal disasters. Science, 309(5737), 1036–1039.Find this resource:

Adger, W. N., Quinn, T., Lorenzoni, I., Murphy, C., & Sweeney, J. (2013). Changing social contracts in climate-change adaptation. Nature Climate Change, 3(4), 330–333.Find this resource:

Aerts, J. C. J. H., Botzen, W. J. W., Emanuel, K., Lin, N., de Moel, H., & Michel-Kerjan, E. O. (2014). Evaluating flood resilience strategies for coastal megacities. Science, 344(6183), 473–475.Find this resource:

AghaKouchak, A., Feldman, D., Hoerling, M., Huxman T., & Lund J. (2015). Recognizing anthropogenic droughts. Nature, 524, 409–411.Find this resource:

Apel, H., Aronica, G. T., Kreibich, H., & Thieken, A. H. (2009). Flood risk analyses: How detailed do we need to be? Natural Hazards, 49(1), 79–98.Find this resource:

Bianchizza, C., & Frigerio, S. (2013). Domination of or adaptation to nature? A lesson we can still learn from the Vajont. Italian Journal of Engineering Geological Environment, 6, 523–531.Find this resource:

Blair, P., & Buytaert, W. (2016). Socio-hydrological modelling: A review asking “why, what, and how?” Hydrology and Earth System Sciences, 20, 443–478.Find this resource:

Blöschl, G., Nester, T., Komma, J., Parajka, J., & Perdigão, R. A. P. (2013). The June 2013 flood in the Upper Danube Basin, and comparisons with the 2002, 1954, and 1899 floods. Hydrology and Earth System Sciences, 17, 5197–5212.Find this resource:

Bohensky, E., & Leitch, A. (2014). Framing the flood: A media analysis of themes of resilience in the 2011 Brisbane flood. Regional Environmental Change, 14(2), 475–488.Find this resource:

Brown, C. (2007). Differential equations: A modelling approach. Quantitative Applications in the Social Sciences. Thousand Oaks: SAGE.Find this resource:

Burton, C., & Cutter, S. L. (2008). Levee failures and social vulnerability in the Sacramento-San Joaquin Delta area, California. Natural Hazards Review, 9(3), 136–149.Find this resource:

Da Deppo, L., Datei, C., & Salandin, P. (2004). Sistemazione dei corsi d’acqua. Padua, Italy: Cortina Padova (In Italian).Find this resource:

Dankers, R., et al. (2014). First look at changes in flood hazard in the Inter-Sectoral Impact Model Intercomparison Project ensemble. Proceedings of the National Academy of Sciences, 111(9), 3257–3261.Find this resource:

de Moel, H., Aerts, J. C. J. H., & Koomen, E. (2011). Development of flood exposure in the Netherlands during the 20th and 21st century. Global Environmental Change, 21(2), 620–627.Find this resource:

Delle Rose, M. (2012). Decision-making errors and sociopolitical disputes over the Vajont Dam disaster. Disaster Advancement, 5, 144–152.Find this resource:

Di Baldassarre, G., Castellarin, A., and Brath, A. (2009). Analysis of the effects of levee heightening on flood propagation: example of the River Po, Italy. Hydrological Sciences Journal, 54(6), 1007–1017.Find this resource:

Di Baldassarre, G., Brandimarte, L., & Beven, K. (2016). The seventh facet of uncertainty: Wrong assumptions, unknowns, and surprises in the dynamics of human-water systems. Hydrological Sciences Journal, 61(9), 1748–1758.Find this resource:

Di Baldassarre, G., Kooy, M., Kemerink, J. S., & Brandimarte, L. (2013a). Towards understanding the dynamic behaviour of floodplains as human-water systems. Hydrology and Earth System Sciences, 17(8), 3235–3244.Find this resource:

Di Baldassarre, G., Montanari, A., Lins, H., Koutsoyiannis, D., Brandimarte, L., & Blöschl, G. (2010). Flood fatalities in Africa: From diagnosis to mitigation. Geophysical Research Letters, 37(22), L22402.Find this resource:

Di Baldassarre, G., Viglione, A., Carr, G., Kuil, L., Salinas, J. L., & Blöschl, G. (2013b). Socio-hydrology: Conceptualising human-flood interactions. Hydrology and Earth System Sciences, 17(8), 3295–3303.Find this resource:

Di Baldassarre, G., Viglione, A., Carr, G., Kuil, L., Yan, K., Brandimarte, L. et al. (2015). Perspectives on socio-hydrology: Capturing feedbacks between physical and social processes. Water Resources Research, 5(6), 4770–4781.Find this resource:

Di Baldassarre, G., Yan, K., Ferdous, M. R., & Brandimarte, L. (2014). The interplay between human population dynamics and flooding in Bangladesh: A spatial analysis. Proceedings of the ICWRS, Bologna, Italy.

Driscoll, D. L., Appiah-Yeboah, A., Salib, P., & Rupert, D. J. (2007). Merging qualitative and quantitative data in mixed methods research: How to and why not. Ecological and Environmental Anthropology, 3(1).Find this resource:

Eiser, J. R., Bostrom, A., Burton, I., Johnston, D., McClure, J., Paton, D., et al. (2012). Risk interpretation and action: A conceptual framework for research in the context of natural hazards. International Journal of Disaster Risk Reduction.Find this resource:

Elshafei, Y., Sivapalan, M., Tonts, M., & Hipsey, M. R. (2014). A prototype framework for models of socio-hydrology: Identification of key feedback loops and parameterisation approach, Hydrology and Earth System Sciences, 18, 2141–2166.Find this resource:

Folke, C., Hahn, T., Olsson, P., & Norberg, J. (2005). Adaptive governance of social-ecological systems. Annual Review of Environment and Resources, 30, 441–473.Find this resource:

Gilbert, N. (2008). Agent based models. Quantitative Applications in the Social Sciences. Thousand Oaks, CA: SAGE.Find this resource:

Gilbert, N., & Terna, P. (2000). How to build and use agent-based models in social science. Mind & Society, 1, 57–72.Find this resource:

Gober, P., & Wheater, H. S. (2015). Debates: Perspectives on socio-hydrology: Modeling flood risk as a public policy problem. Water Resources Research, 51, 4782–4788.Find this resource:

Grames, J., Prskawetz, A., Grass, D., Viglione, A., & Blöschl, G. (2015). Modelling the interaction between flooding events and economic growth. Ecological Economics, 129, 193–209.Find this resource:

Heine, R., & Pinter, N. (2012). Levee effects upon flood levels: An empirical assessment. Hydrological Processes, 26, 3225–3240.Find this resource:

Hinkel, J., Lincke, D., Vafeidis, A. T., Perrette, M., Nicholls, R. J., Tol, R. S. J., et al. (2014). Coastal flood damage and adaptation costs under 21st-century sea-level rise. Proceedings of the National Academy of Sciences, 111(9), 3292–3297.Find this resource:

Intergovernmental Panel on Climate Change (IPCC). (2012). Managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge, U.K.: Cambridge University Press.Find this resource:

Intergovernmental Panel on Climate Change (IPCC). (2014). Climate change 2014: Impacts, adaptation, and vulnerability. Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, U.K.: Cambridge University Press.Find this resource:

Janssen, M. A., & Ostrom, E. (2006). Empirically based, agent-based models. Ecology and Society, 11(2), 37.Find this resource:

Jick, T. D. (1979). Mixing qualitative and quantitative methods: Triangulation in action. Administrative Science Quarterly, 24(4), 602–611.Find this resource:

Johnson, C., Tunstall, S., & Penning-Rowsell, E. (2005). Floods as catalysts for policy change: Historical lessons from England and Wales. International Journal of Water Resources Development, 21(4), 561–575.Find this resource:

Jongman, B., Hochrainer-Stigler, S., Feyen, L., Aerts, J. C. J. H., Mechler, R., Botzen, W. J. W., et al. (2014). Increasing stress on disaster-risk finance due to large floods. Nature Climate Change, 4(4), 264–268.Find this resource:

Kaldor, N. (1957). A model of economic growth. The Economic Journal, 67(268), 591–624.Find this resource:

Kates, R. W., Colten, C. E., Laska, S., & Leatherman, S. P. (2006). Reconstruction of New Orleans after Hurricane Katrina: A research perspective. Proceedings of the National Academy of Sciences, 103(40), 14653–14660.Find this resource:

Lall, U. (2014). Debates: The future of hydrological sciences: A (common) path forward? One water. One world. Many climes. Many souls. Water Resources Research, 50(6), 5335–5341.Find this resource:

Lane, S. N., Odoni, N., Landstrom, C., Whatmore, S. J., Ward, N., & Bradley, S., (2011). Doing flood risk science differently: An experiment in radical scientific method. Transactions of the Institute of British Geographers, 36(1), 15–26.Find this resource:

Liu, D., Tian, F., Lin, M., & Sivapalan, M. (2015). A conceptual socio-hydrological model of the co-evolution of humans and water: Case study of the Tarim River basin, western China. Hydrology and Earth System Sciences, 19, 1035–1054.Find this resource:

Liu, J., Dietz, T., Carpenter, S. R., Alberti, M., Folke, C., Moran, E., et al. (2007). Complexity of coupled human and natural systems. Science, 317(5844), 1513–1516.Find this resource:

Loucks, D. P. (2015). Debates: Perspectives on socio-hydrology: Simulating hydrologic-human interactions. Water Resources Research, 51, 4789–4794.Find this resource:

Ludy, J., & Kondolf, G. M. (2012). Flood risk perception in lands “protected” by 100-year levees. Natural Hazards, 61(2), 829–842.Find this resource:

Makridakis, S., & Taleb, N. (2009a). Living in a world of low levels of predictability. International Journal of Forecasting, 25, 840–844.Find this resource:

Makridakis, S., & Taleb, N. (2009b). Decision making and planning under low levels of predictability. International Journal of Forecasting, 25(4), 716–733.Find this resource:

Mateo, C. M., Hanasaki, N., Komori, D., Komori, D., Tanaka, K., Kiguchi, M., et al. (2014). Assessing the impacts of reservoir operation to floodplain inundation by combining hydrological, reservoir management, and hydrodynamic models. Water Resources Research, 50, 7245–7266.Find this resource:

Me-Bar, Y., & Valdez, F. J. (2003). Recovery time after a disaster and the ancient Maya. Journal of Archeological Sciences, 31, 1311–1324.Find this resource:

Mechler, R., & Bouwer, L. M. (2015). Understanding trends and projections of disaster losses and climate change: Is vulnerability the missing link? Climatic Change, 133(1), 23–35.Find this resource:

Merz, B., Vorogushyn, S., Lall, U., Viglione, A., & Blo€schl, G. (2015). Charting unknown waters—On the role of surprise in flood risk assessment and management. Water Resources Research, 51.Find this resource:

Montanari, A., Young, H. H. G., Savenije, D., Hughes, T., Wagener, L. L. Ren, D., et al. (2013). “Panta Rhei—everything flows”: Change in hydrology and society: The IAHS Scientific Decade 2013–2022. Hydrological Science Journal, 58(6), 1256–1275.Find this resource:

Montz, B., & Tobin, G. (2008). Livin’ large with levees: Lessons learned and lost. Natural Hazards Review, 9(3), 150–157.Find this resource:

Myers, C., Slack, T., & Singelmann, J. (2008). Social vulnerability and migration in the wake of disaster: The case of Hurricanes Katrina and Rita. Population and Environment, 29(6), 271–291.Find this resource:

Nefedov, S. A. (2004). A model of demographic cycles in traditional societies: The case of ancient China. Social Evolution & History, 3(1), 69–80.Find this resource:

O’Connell, P. E., & O’Donnell, G. (2014). Towards modelling flood protection investment as a coupled human and natural system. Hydrology and Earth System Sciences, 18, 155–171.Find this resource:

Opperman, J. J., Galloway, G. E., Fargione, J., Mount, J. F., Richter, B. D., & Secchi, S. (2009). Sustainable floodplains through large-scale reconnection to rivers. Science, 326(5959), 1487–1488.Find this resource:

Ostrom, E. (2009). A general framework for analyzing sustainability of social-ecological systems. Science, 325, 419–422.Find this resource:

Pahl-Wostl, C., Becker, G., Knieper, C., & Sendzimir, J. (2013). How multilevel societal learning processes facilitate transformative change: A comparative case study analysis on flood management. Ecology and Society, 18(4), 58.Find this resource:

Pande, S., & Savenije, H. H. G. (2016). A sociohydrological model for smallholder farmers in Maharashtra, India. Water Resources Research, 52, 1923–1947.Find this resource:

Penning-Rowsell, E., Johnson, C., & Tunstall, S. (2006). “Signals” from pre-crisis discourse: Lessons from UK flooding for global environmental policy change? Global Environmental Change, 16, 323–339.Find this resource:

Penning-Rowsell, E. C (1996). Flood-hazard response in Argentina. Geophysical Review, 86, 72–90.Find this resource:

Scheffer, M., Carpenter, S. R., Foley, J. A., Folke, C., & Walker, B. (2001). Catastrophic shifts in ecosystems, Nature, 413, 591–596.Find this resource:

Scolobig, A., De Marchi, B., & Borga, M. (2012). The missing link between flood risk awareness and preparedness: Findings from case studies in an Alpine Region. Natural Hazards, 63, 499–520.Find this resource:

Sendai Framework for Disaster Risk Reduction. (2015). United Nations Office for Disaster Risk Reduction.

Sivapalan, M., & Bloeschl, G. (2015). Time scale interactions and the coevolution of humans and water. Water Resources Research, 51, 6988–7022.Find this resource:

Sivapalan, M., Savenije, H. H. G., & Blöschl, G. (2012). Socio-hydrology: A new science of people and water. Hydrological Processes, 26(8), 1270–1276.Find this resource:

Srinivasan, V., Lambin, E. F., Gorelick, S. M., Thompson, B. H., & Rozelle, S. (2012). The nature and causes of the global water crisis: Syndromes from a meta-analysis of coupled human-water studies. Water Resources Research, 48(10).Find this resource:

Tainter, J. A. (2004). Plotting the downfall of society. Nature, 427, 488–489.Find this resource:

Taleb, N. N. (2007). The black swan: The impact of the highly improbable. London: Penguin.Find this resource:

Troy, T. J., Pavao-Zuckerman, M., & Evans, T. P. (2015). Debates: Perspectives on socio-hydrology: Socio-hydrologic modeling: Tradeoffs, hypothesis testing, and validation. Water Resources Research, 51, 4806–4814.Find this resource:

Turchin, P. (2003). Historical dynamics: Why states rise and fall. Princeton, NJ: Princeton University Press.Find this resource:

Turchin, P., & Korotayev, A. (2006). Population dynamics and internal warfare: A reconsideration. Social Evolution & History, 5(2), 112–147.Find this resource:

Turner, B. L., Kasperson, R. E., Matson, P. A., McCarthy, J. J., Corell, R. W., Christensen, L., et al. (2003). A framework for vulnerability analysis in sustainability science. Proceedings of the National Academy of Sciences, 100(14), 8074–8079.Find this resource:

Turner, M. D. (2016). Climate vulnerability as a relational concept. Geoforum, 68, 29–38.Find this resource:

Van den Honert, R. C., & McAneney, J. (2011). The 2011 Brisbane floods: Causes, impacts, and implications. Water, 3, 1149–1173.Find this resource:

Van Dijk, A. I. J. M., Beck, H. E., Crosbie, R. S., de Jeu, R. A. M., Liu, Y. Y., Podger, G. M., et al. (2013). The Millennium Drought in Southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society, Water Resources Research, 49, 1040–1057.Find this resource:

Van Emmerik, T. H. M., Li, Z., Sivapalan, M., Pande, S., Kandasamy, J., Savenije, H. H. G., Chanan, A., et al. (2014). Socio-hydrologic modeling to understand and mediate the competition for water between agriculture development and environmental health: Murrumbidgee River Basin, Australia. Hydrology and Earth System Sciences, 18, 4239–4259.Find this resource:

Van Loon, A. F., Gleeson, T., Clark, J., van Dijk, A. I. J. M., Stahl, K., Hannaford, J., et al. (2016). Drought in the Anthropocene. Nature Geoscience, 9, 89–91.Find this resource:

Viglione, A., Di Baldassarre, G., Brandimarte, L., Kuil, L., Carr, G., Salinas, J. L., et al. (2014). Insights from socio-hydrology modelling on dealing with flood risk: Roles of collective memory, risk-taking attitude, and trust. Journal of Hydrology, 518, 71–82.Find this resource:

Viparelli, M., & Merla, G. (1968). L’onda di piena seguita alla frana del Vajont. Atti dell’Accademia Pontaniana (in Italian) (vol. 17, p. 229). Italy: Napoli.Find this resource:

Vis, M., Klijn, F., De Bruijn, K. M., & Van Buuren, M. (2003). Resilience strategies for flood risk management in the Netherlands. International Journal of River Basin Management, 1(1), 33–40.Find this resource:

Wachinger, G., Renn, O., Begg, C., & Kuhlicke, C. (2012). The risk perception paradox-implications for governance and communication of natural hazards. Risk Analysis, 33(6), 1049–1065.Find this resource:

Wagener, T., Sivapalan, M., Troch, P. A., McGlynn, B. L., Harman, C. J., Gupta, H. V., et al (2010). The future of hydrology: An evolving science for a changing world. Water Resources Research, 46.Find this resource:

Werner, B. T., & McNamara, D. E. (2007). Dynamics of coupled human-landscape systems. Geomorphology, 91(3–4), 393–407.Find this resource:

White, G. F. (1945). Human adjustments to floods. Chicago: University of Chicago Press.Find this resource:

Wilby, R. L., & Dessai, S. (2010). Robust adaptation to climate change. Weather, 65, 180–185.Find this resource:

Wind, H. G., Nierop, T. M., de Blois, C. J., & de Kok, J. L. (1999). Analysis of flood damages from the 1993 and 1995 Meuse Floods. Water Resources Research, 35(11), 3459–3465.Find this resource:

Winsemius, H. C., Aerts, J. C. J. H., Van Beek, L. P. H., Bierkens, M. F. P., Bouwman, A., Jongman, B., et al. (2015). Global drivers of future river flood risk. Nature Climate Change, 6(4), 381–385.Find this resource:

Winsemius, H. C., Van Beek, L. P. H., Jongman, B., Ward, P. J., & Bouwman, A. (2013). A framework for global river flood risk assessments. Hydrology and Earth System Sciences, 17(5), 1871–1892.Find this resource: